A PyTorch-powered differentiable image reconstruction/optimization toolbox
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Updated
Jul 28, 2024 - Python
A PyTorch-powered differentiable image reconstruction/optimization toolbox
A toolkit for auto-generation of OpenAI Gym environments from RDDL description files.
TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software
Model-based Reinforcement Learning Framework
Skill-based Model-based Reinforcement Learning (CoRL 2022)
CaDM: Context-aware Dynamics Model for Generalization in Model-based Reinforcement Learning
Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"
Model-based Policy Gradients
Algorithmic Methods of Model-based Medical Image Segmentation Using Python
Model-based AI approach for network and service coordination leveraging uncertain traffic forecasts
Free-breathing myocardial T1 mapping with Physically-Constrained Motion Correction
Model-based tomographic reconstruction for different acquisition geometries
JAX compilation of RDDL description files, and a differentiable planner in JAX.
A Model-based Agent, for chinese speech recognize.
Project for the course "Foundations of Reinforcement Learning" 2021 at ETH Zurich
The goal of this repository is to provide a gym-compatible library to easily perform model-based Reinforcement Learning experiments using PyTorch. The library makes it easier to create learnable environments and ensembles of networks that can be used to learn the dynamics of an environment.
Simulating a futuristic package delivery service using drones.
A Model-Based Signal Processing Library Working With Windowed Linear State-Space and Polynomial Signal Models.
This is the code repository for my thesis
Gurobi compilation of RDDL description files to mixed-integer programs, and optimization tools.
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